Heat pump dual-combined supply system, operation control method and device thereof and medium

Abstract

The invention discloses a heat pump dual-combined supply system, an operation control method and device thereof and a medium, and is applied to the technical field of household appliances, and the method comprises the following steps: acquiring operation data of the heat pump dual-combined supply system in a current time period; according to the operation data of the heat pump dual-combined supply system in the current time period, the generated energy and the required energy consumption of the heat pump dual-combined supply system in the next time period are predicted, so that an energy prediction result and an energy consumption prediction result are obtained; predicting the room state of the next time period according to the energy prediction result and the operation data to obtain a room state prediction result; and parameter optimization is carried out according to the energy consumption prediction result and the room state prediction result so as to determine target control parameters, and the heat pump dual-combined supply system is controlled to operate according to the target control parameters in the next time period. According to the invention, the technical problem of energy waste during operation of the heat pump dual-combined supply system is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of household appliances, and particularly relates to a heat pump two-supply system, a running control method and device thereof, and a medium. BACKGROUND

[0002] The heat pump two-supply system is a heat pump two-supply system for central air conditioning and floor heating, that is, the central air conditioner at the top uses a "fluorine system" to realize refrigeration or heating, and the bottom uses a "water system" to supply cooling or heating. In summer, the "fluorine system" is generally started alone to refrigerate, and in winter, the "fluorine system" and the "water system" can be started simultaneously to heat. The current heat pump two-supply system uses only whether the room temperature reaches the set value to control the start and stop of the shunt water valve at the location of the room when using floor heating in winter, and the water outlet temperature and other parameters are determined by the user's set value. The "water system" is controlled to start and stop and run according to the user's setting and the feedback of the water outlet temperature, return water temperature and the like, and in some working conditions, the heat pump two-supply system will run inefficiently due to unreasonable user setting, resulting in energy waste and lack of energy saving. SUMMARY

[0003] The present application provides a heat pump two-supply system, a running control method and device thereof, and a medium to solve the technical problem of energy waste caused by the operation of the heat pump two-supply system.

[0004] In the first aspect of the present application, a running control method of a heat pump two-supply system is provided, comprising: obtaining running data of the heat pump two-supply system in a current time period; predicting the generated energy and required energy consumption of the heat pump two-supply system in a next time period according to the running data to obtain energy prediction results and energy consumption prediction results; predicting the room state of the next time period according to the energy prediction results and the running data to obtain room state prediction results; performing parameter optimization according to the energy consumption prediction results and the room state prediction results to determine target control parameters, and controlling the heat pump two-supply system to run according to the target control parameters in the next time period.

[0005] In combination with the first aspect, in some embodiments, the prediction of the generated energy and required energy consumption of the heat pump two-supply system in the next time period according to the running data to obtain energy prediction results and energy consumption prediction results comprises: determining a control parameter given range of the next time period according to the running data; predicting the generated energy and required energy consumption of the heat pump two-supply system in the next time period based on the control parameter given range to obtain the energy prediction results and the energy consumption prediction results.

[0006] In some embodiments of the first aspect, the predicting the generated energy and the required energy consumption of the heat pump two-in-one system based on the control parameter given range in the next time period comprises: generating various parameter value candidate combinations of the control parameter in the control parameter given range; and predicting the generated energy and the required energy consumption of the heat pump two-in-one system based on each of the parameter value candidate combinations in the next time period to obtain an energy prediction value corresponding to the parameter value candidate combination in the energy prediction result and an energy consumption prediction value corresponding to the parameter value candidate combination in the energy consumption prediction result.

[0007] In some embodiments of the first aspect, the predicting the generated energy and the required energy consumption of the heat pump two-in-one system based on each of the parameter value candidate combinations in the next time period comprises: obtaining target mapping data from pre-stored mapping data, wherein the target mapping data is a mapping between a plurality of target frequency points and fitting coefficients, the plurality of target frequency points are related to an actual operating frequency of a compressor of the heat pump two-in-one system in a current time period, and the operating data includes the actual operating frequency; and predicting the generated energy and the required energy consumption of the heat pump two-in-one system based on each of the parameter value candidate combinations according to the target mapping data to obtain M data pairs, wherein each of the data pairs is a correspondence between a frequency point and an energy calculation value and an energy consumption calculation value, and M is an integer greater than 1; and performing interpolation operation according to the actual operating frequency and the M data pairs to obtain the energy prediction value corresponding to the parameter value candidate combination in the energy prediction result and the energy consumption prediction value corresponding to the parameter value candidate combination in the energy consumption prediction result.

[0008] In some embodiments of the first aspect, the target mapping data comprises M sets of mapping data, and the predicting the generated energy and the required energy consumption of the heat pump two-in-one system based on each of the parameter value candidate combinations comprises: for each set of mapping data in the target mapping data, determining a correction coefficient matched with the set of mapping data according to the parameter value candidate combination, and determining a data pair matched with the set of mapping data according to a nominal value of the heat pump two-in-one system and the correction coefficient matched with the set of mapping data.

[0009] In some embodiments of the first aspect, the correction coefficient matched by each set of mapping data in the target mapping data comprises an energy correction coefficient and an energy consumption correction coefficient; and the determining, for each set of mapping data in the target mapping data, a data pair matched with the set of mapping data according to the nominal values of the heat pump two-in-one system and the correction coefficient matched with the set of mapping data comprises: determining, for each set of mapping data in the target mapping data, an energy calculation value of the set of mapping data according to the energy nominal value of the heat pump two-in-one system and the energy correction coefficient matched with the set of mapping data, and determining an energy consumption calculation value of the set of mapping data according to the energy consumption nominal value of the heat pump two-in-one system and the energy consumption correction coefficient matched with the set of mapping data; and obtaining, for each set of mapping data in the target mapping data, a data pair matched with the set of mapping data based on a target frequency point in the set of mapping data and the energy calculation value and the energy consumption calculation value determined based on the set of mapping data.

[0010] In some embodiments of the first aspect, the interpolating according to the actual operating frequency and the M data pairs to obtain an energy prediction value corresponding to the candidate combination of the parameter value in the energy prediction result and an energy consumption prediction value corresponding to the candidate combination of the parameter value in the energy consumption prediction result comprises: interpolating energy according to the actual operating frequency and the M data pairs to obtain the energy prediction value corresponding to the candidate combination of the parameter value in the energy prediction result; and interpolating energy consumption according to the actual operating frequency and the M data pairs to obtain the energy consumption prediction value corresponding to the candidate combination of the parameter value in the energy consumption prediction result.

[0011] In some embodiments of the first aspect, the determining, for each set of mapping data in the target mapping data, a correction coefficient matched with the set of mapping data according to the candidate combination of the parameter value comprises: obtaining an outdoor temperature of a current time period from the operating data; determining, for each set of mapping data in the target mapping data, a multi-dimensional energy correction factor and a multi-dimensional energy consumption correction factor affecting the generated energy and the required energy consumption of the heat pump two-in-one system based on the candidate combination of the parameter value according to the outdoor temperature of the current time period, the fitting coefficient in the set of mapping data and the candidate combination of the parameter value in the given range of the control parameter; determining, for each set of mapping data in the target mapping data, the energy correction coefficient matched with the set of mapping data according to the multi-dimensional energy correction factor obtained based on the set of mapping data, and the energy consumption correction coefficient matched with the set of mapping data according to the multi-dimensional energy consumption correction factor obtained based on the set of mapping data.

[0012] In some embodiments of the first aspect, the multi-dimensional energy correction factor and the multi-dimensional energy consumption correction factor each include a correction factor related to an outdoor temperature and a water outlet temperature of a hydraulic module in the heat pump two-in-one system, a correction factor related to a water flow of the hydraulic module, and a correction factor related to a defrosting process of an outdoor heat exchanger in the heat pump two-in-one system.

[0013] In some embodiments of the first aspect, the room state prediction result includes a room temperature prediction result; and the predicting the room state of the next time period according to the energy prediction result and the operation data to obtain the room state prediction result includes: obtaining an outdoor temperature of the current time period and a room temperature of a target room in the current time period from the operation data, the target room being a room in the heat pump two-in-one system in which a water terminal is in an enabled state by a user; inputting the energy prediction result, the outdoor temperature of the current time period, and the room temperature of the target room into a room temperature prediction model, so that the room temperature prediction model predicts a room temperature of the target room in the next time period to obtain the room temperature prediction result.

[0014] In some embodiments of the first aspect, the room temperature prediction model is a calculation model related to a heat storage characteristic of an envelope of the target room, an air energy in the target room, and a heat source in the target room, and is established according to a law of conservation of energy.

[0015] In some embodiments of the first aspect, the room temperature prediction model has an expression as follows:

[0016]

[0017] wherein T k+1 is a room temperature prediction value that can be reached by the target room for a parameter value candidate combination; a is an air energy in the target room, b is a parameter related to the energy prediction result and a heat source energy in the target room; UA loss k is a parameter representing the heat storage characteristic of the envelope of the target room, T o k is the outdoor temperature of the current time period.

[0018] In combination with the first aspect, in some embodiments, the parameter optimization according to the energy consumption prediction result and the room state prediction result to determine the target control parameter comprises: inputting the energy consumption prediction result and the room state prediction result into a pre-established optimization model, so that the optimization model takes the room temperature within a temperature given range as a constraint condition, the energy consumption of the heat pump two-source heat supply system as an optimization target, and performs parameter optimization calculation on the control parameter given range according to the energy consumption prediction result and the room state prediction result, to obtain the target control parameter within the control parameter given range.

[0019] In a second aspect of the present application, a heat pump two-source heat supply system operation control device is provided, comprising: a data acquisition unit configured to acquire operation data of the heat pump two-source heat supply system in a current time period; a first prediction unit configured to predict generated energy and required energy consumption of the heat pump two-source heat supply system in a next time period according to the operation data, to obtain an energy prediction result and an energy consumption prediction result; a second prediction unit configured to predict a room state in the next time period according to the energy prediction result and the operation data, to obtain a room state prediction result; an optimization unit configured to perform parameter optimization according to the energy consumption prediction result and the room state prediction result, to determine a target control parameter; and a control unit configured to control the heat pump two-source heat supply system to operate according to the target control parameter in the next time period.

[0020] In a third aspect of the present application, a heat pump two-source heat supply system is provided, comprising: an outdoor unit; a water module and a plurality of air conditioning terminals connected with the outdoor unit respectively; a plurality of water terminals connected with the outdoor unit through the water module; a processor; and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the instructions to implement the heat pump two-source heat supply system operation control method of any of the embodiments of the first aspect.

[0021] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, wherein the program is executed by a processor to implement the heat pump two-source heat supply system operation control method of any of the embodiments of the first aspect.

[0022] The one or more technical solutions provided by the embodiments of the present application at least achieve the following technical effects or advantages:

[0023] The embodiment of the present application obtains operation data of the heat pump two-supply system in a current time period; predicts energy generated and energy consumption required for the heat pump two-supply system to operate in a next time period according to the operation data, to obtain energy prediction results and energy consumption prediction results; predicts room states in the next time period according to the energy prediction results and the operation data, to obtain room state prediction results; performs parameter optimization according to the energy consumption prediction results and the room state prediction results, to determine target control parameters, and controls the heat pump two-supply system to operate according to the target control parameters in the next time period. The above technical solution actively predicts energy, energy consumption and room states by using operation data of the heat pump two-supply system, to realize optimization of control parameters for controlling the heat pump two-supply system in the next time period, so that the heat pump two-supply system is no longer operated according to fixed parameters set by a user, and therefore, the heat pump two-supply system can operate with high energy efficiency and can be more energy-saving. Moreover, since the heat pump two-supply system is no longer controlled completely according to fixed parameters set by the user, room temperature fluctuation caused by frequent start and stop of water end of the heat pump two-supply system due to unreasonable setting of the user can be avoided to a certain extent, and therefore, control stability is improved and temperature in the room is more stable. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0025] Figure 1 The structure of the heat pump two-supply system in some embodiments of the present application is shown;

[0026] Figure 2 The application scenario of the operation control method of the heat pump two-supply system in some embodiments of the present application is shown;

[0027] Figure 3 The flow of the operation control method of the heat pump two-supply system in some embodiments of the present application is shown;

[0028] Figure 4 The topology structure of the control logic of the operation control method of the heat pump two-supply system in some embodiments of the present application is shown;

[0029] Figure 5A The room temperature comparison of the operation control method of the heat pump two-supply system in some embodiments of the present application and related technologies is shown;

[0030] Figure 5B The room PMV comparison of the operation control method of the heat pump two-supply system in some embodiments of the present application and related technologies is shown;

[0031] Figure 5C This paper illustrates a comparison of the operation control method of the heat pump dual-supply system in some embodiments of the present invention with the outlet water temperature of related technologies;

[0032] Figure 6 The structure of the operation control device of the heat pump dual-supply system in some embodiments of the present invention is shown;

[0033] Figure 7 The control structure of a heat pump dual-supply system in some embodiments of the present invention is shown. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0035] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0036] This invention provides an operation control method for a combined heat pump and combined heat and power system, such as... Figure 1 As shown, Figure 1The diagram illustrates the structure of a heat pump dual-supply system in some embodiments of the present invention. This system includes an outdoor unit, a hydraulic module connected to the outdoor unit, and multiple air conditioning terminals, as well as multiple water terminals connected to the outdoor unit via the hydraulic module. The water terminals can be underfloor heating coils installed beneath the floor. The number of water terminals and air conditioning terminals is determined by the number of rooms in the installation environment. At least the outdoor unit and each air conditioning terminal constitute a refrigerant system, where each air conditioning terminal is also an indoor air conditioning unit. The refrigerant system directly transfers the energy stored in the refrigerant of the outdoor unit to the indoor space. The outdoor unit, hydraulic module, and water terminals constitute a water system. The hydraulic module transfers the energy stored in the refrigerant of the outdoor unit to the water, and the water with stored energy is then released to the indoor space through the water terminals. It is understood that there is a one-to-one correspondence between the air conditioning terminals and the water terminals; that is, in multiple rooms, one air conditioning terminal and one water terminal are installed in each room.

[0037] The operation control method for the combined heat pump and combined heat and power system provided in this embodiment of the invention can be executed by a cloud device or edge device that has established a communication connection with the combined heat pump and combined heat and power system, such as... Figure 2 As shown, Figure 2 The present invention illustrates application scenarios of the operation control method for a combined heat pump and combined heat and power system according to some embodiments. These application scenarios include at least an edge device (or cloud device) and a combined heat pump and combined heat and power system, and may further include a user terminal. The edge device (or cloud device) establishes a communication connection with the combined heat pump and combined heat and power system through a gateway, and also establishes a communication connection with the user terminal (e.g., a mobile phone or tablet) through the gateway. Of course, in scenarios where the combined heat pump and combined heat and power system has sufficient computing power, the method can also be executed directly on the local side of the system.

[0038] like Figure 3 As shown, Figure 3 The flowchart of the operation control method of a heat pump dual-supply system in some embodiments of the present invention is shown. The operation control method of the heat pump dual-supply system includes the following steps S101 to S104.

[0039] S101: Obtain the operating data of the heat pump dual-supply system for the current time period.

[0040] In some implementations, the operating data of the combined heat pump and power system is acquired periodically according to a preset time step. This ensures that operating data generated by the system during each time period divided by the time step can be obtained, with the current time period being the closest to the current moment. It is understood that the time step is preset according to actual needs, and the time step can be between 10 and 60 minutes; for example, a time step of 10 minutes, 20 minutes, 40 minutes, or 60 minutes can be used.

[0041] like Figure 4 As shown, Figure 4 The topology of the control logic of the operation control method in some embodiments of the present invention is shown. In the scenario where the operation control method of the heat pump dual-supply system is applied to cloud devices or edge devices, the heat pump dual-supply system collects various operation data generated by its own operation according to a preset time step, and uploads them to the cloud devices or edge devices through a gateway (edge ​​gateway, Wi-Fi, etc.) so that the cloud devices or edge devices can obtain the operation data of the heat pump dual-supply system in the current time period.

[0042] In some implementations, the operating data of the heat pump dual-supply system in the current time period may include the actual operating frequency of the compressor, outdoor temperature, room temperature, outlet water temperature of the hydraulic module, return water temperature and water flow rate, and the on / off signals of various valves (including throttle valves connected to the hydraulic module, diversion valves connecting the water terminals of each room to the hydraulic module, etc.).

[0043] S102: Based on the operating data of the combined heat pump and power system in the current time period, predict the energy generated and energy consumption required by the combined heat pump and power system in the next time period to obtain energy prediction results and energy consumption prediction results.

[0044] In some implementations, step S102 may include: determining the given range of control parameters for the next time period based on the operating data of the combined heat pump and power system in the current time period; predicting the energy generated and energy consumption required by the combined heat pump and power system in the next time period based on the given range of control parameters, so as to obtain energy prediction results and energy consumption prediction results.

[0045] Understandably, the operating data for the current time period is input into a pre-established optimization model so that the optimization model can determine the given range of control parameters for the next time period based on the operating data for the current time period; and the given range of control parameters is input into a pre-established energy consumption prediction model so that the energy consumption prediction model can predict the energy generated and required by the heat pump combined heat and power system in the next time period based on the given range of control parameters.

[0046] In some implementations, predicting the energy generated and energy consumption required by the combined heat pump and power system in the next time period based on a given range of control parameters to obtain energy prediction results and energy consumption prediction results may include: generating various candidate combinations of control parameter values ​​within the given range of control parameters; for each candidate combination of parameter values, predicting the energy generated and energy consumption required by the combined heat pump and power system in the next time period based on that candidate combination of parameter values ​​to obtain the energy prediction value corresponding to that candidate combination of parameter values ​​in the energy prediction results, and the energy consumption prediction value corresponding to that candidate combination of parameter values ​​in the energy consumption prediction results.

[0047] It is understood that the given range of control parameters can include the given range of at least one dimension of control parameters. In some implementations, the dimension of the control parameter includes the outlet water temperature of the hydraulic module and the opening time of the diversion valve connected between the hydraulic module and the water terminal in each room, which controls whether water flows through the water terminal in that room. Therefore, the given range of control parameters can include: the given range of the outlet water temperature of the hydraulic module and the given range of the opening time of the diversion valve in the target room. There are multiple candidate combinations of parameter values, each of which is a combination of a temperature value of the outlet water temperature of the hydraulic module and a time value of the opening time of the diversion valve in the target room. It should be noted that different candidate combinations of parameter values ​​have the same parameter type but different parameter values.

[0048] In some implementations, to more accurately predict the energy generated and energy consumption required for each candidate combination of parameter values, interpolation can be used to determine the predicted energy consumption and energy values ​​for each candidate combination of parameter values: Target mapping data is obtained from pre-stored mapping data; for each candidate combination of parameter values, the energy generated and energy consumption required for the combined heat pump system operating based on that candidate combination of parameter values ​​are predicted according to the target mapping data, to obtain M data pairs matching that candidate combination of parameter values, where each data pair corresponds to a frequency point and the calculated energy value and energy consumption value; interpolation is performed based on the actual operating frequency of the compressor and the M data pairs to obtain the predicted energy value corresponding to that candidate combination of parameter values ​​in the energy prediction results, and the predicted energy consumption value corresponding to that candidate combination of parameter values ​​in the energy consumption prediction results.

[0049] It is understandable that each data pair is a frequency point and the energy calculation value and energy consumption calculation value calculated based on the fitted data mapped from that frequency point, for example: (frequency point A1; energy calculation value B1; energy consumption calculation value C1), (frequency point A2; energy calculation value B2; energy consumption calculation value C2).

[0050] It is understandable that the pre-stored mapping data is the mapping between different frequency points and fitting coefficients within the operating frequency range of the compressor. That is, multiple different frequency points are selected within the operating frequency range of the compressor, and each frequency point is configured with a fitting coefficient suitable for calculating the correction coefficient of that frequency point. The target mapping data is the mapping between multiple target frequency points and fitting coefficients. The selection of each target frequency point is related to the actual operating frequency of the compressor of the heat pump dual-supply system in the current time period. The operating data of the current time period includes the actual operating frequency of the compressor in the current time period. Therefore, the target mapping data is obtained from the pre-stored mapping data, including: obtaining the target mapping data from the pre-stored mapping data based on the actual operating frequency of the compressor in the current time period.

[0051] It is understood that the target mapping data includes M sets, where M is an integer greater than 1. In some implementations, the target mapping data may include only two sets, that is, two sets of mapping data corresponding to two frequency points adjacent to the actual operating frequency of the current time period, with one set of mapping data pre-stored for each frequency point.

[0052] In some implementations, the process of determining M data pairs for each candidate combination of parameter values ​​is the same or similar. For each candidate combination of parameter values, the step of predicting the energy generated and required energy consumption of the combined heat pump system based on the target mapping data to obtain M data pairs may include: for each set of mapping data in the target mapping data, determining a correction coefficient that matches that set of mapping data based on the candidate combination of parameter values, and determining a data pair that matches that set of mapping data based on the nominal value of the combined heat pump system and the correction coefficient that matches that set of mapping data.

[0053] In some implementations, the correction coefficients for matching each set of target mapping data include an energy correction coefficient and an energy consumption correction coefficient. The nominal values ​​of the combined heat pump and power system include a nominal energy value and a nominal energy consumption value. A data pair matching the set of mapping data is determined based on the nominal values ​​of the combined heat pump and power system and the correction coefficients matching the set of mapping data. This includes: for each set of mapping data in the target mapping data, determining the calculated energy value of the set of mapping data based on the nominal energy value of the combined heat pump and power system and the energy correction coefficients matching the set of mapping data; determining the calculated energy consumption value of the set of mapping data based on the nominal energy consumption value of the combined heat pump and power system and the energy consumption correction coefficients matching the set of mapping data; and for each set of mapping data in the target mapping data, obtaining a data pair matching the set of mapping data based on the target frequency point in the set of mapping data and the calculated energy value and calculated energy consumption value determined based on the set of mapping data.

[0054] In some implementations, for each set of mapping data in the target mapping data, a correction coefficient matching the set of mapping data is determined based on the candidate combination of such parameter values. This includes: obtaining the outdoor temperature for the current time period from the operating data; for each set of mapping data, determining a multidimensional energy correction factor and a multidimensional energy consumption correction factor that affect the energy generated by the heat pump combined heat and power system based on the outdoor temperature for the current time period, the fitting coefficient in the set of mapping data, and the candidate combination of such parameter values ​​within the given range of the control parameters, based on the fitting coefficient in the set of mapping data; and determining an energy correction coefficient matching the set of mapping data based on the multidimensional energy correction factor obtained from the set of mapping data, and determining an energy consumption correction factor matching the set of mapping data based on the multidimensional energy consumption correction factor obtained from the set of mapping data.

[0055] It should be noted that, since the energy generated and energy consumption required by the water system in a combined heat pump and cooling system are affected by the outdoor temperature, the water flow rate and outlet water temperature of the hydraulic module, and the defrosting process of the outdoor heat exchanger in the outdoor unit, the energy correction factors determined for each set of mapping data in the target mapping data include the following dimensions: a first temperature correction factor related to the outdoor temperature and the water temperature of the hydraulic module in the combined heat pump and cooling system, a first flow rate correction factor related to the water flow rate of the hydraulic module, and a first defrosting correction factor related to the defrosting process of the outdoor heat exchanger in the combined heat pump and cooling system. The energy consumption correction factors determined include the following dimensions: a second temperature correction factor related to the outdoor temperature and the outlet water temperature of the hydraulic module in the combined heat pump and cooling system, a second flow rate correction factor related to the water flow rate of the hydraulic module, and a second defrosting correction factor related to the defrosting process of the outdoor heat exchanger in the combined heat pump and cooling system.

[0056] It should be noted that the fitting coefficients in each set of mapped data include multiple sets, and the calculation of the correction factor for each dimension requires the use of a set of fitting coefficients stored in memory.

[0057] For various outlet water temperature values ​​within a given range for the hydraulic module, a first temperature correction factor is determined for each outlet water temperature value using the same set of fitting coefficients, and a second temperature correction factor is also determined for each outlet water temperature value using the same set of fitting coefficients. Both the first and second temperature correction factors can be determined using, but are not limited to, a quadratic polynomial sub-model from the energy consumption prediction model; the only difference between the two is the fitting coefficients. The expressions for the quadratic polynomial sub-model used to determine the first and second temperature correction factors can be found in the following formulas (1) and (2):

[0058] ξ T =a0+a1T water,in +a2T water,in2 +a3T db,o +a4T db,o 2 +a5T water,in T db,o (1)

[0059] ξ T ′=d0+d1T water,in +d2T water,in 2 +d3T db,o +d4T db,o 2 +d5T water,in T db,o (2)

[0060] Where a0 to a5 are a set of fitting coefficients used to determine the first temperature correction factor in this set of mapping data, and d0 to d5 are another set of fitting coefficients used to determine the second temperature correction factor in this set of mapping data, T water,in T represents the outlet water temperature value within a given range for the hydraulic module. db,o The outdoor temperature can be either the dry-bulb temperature of the measured outdoor environment or the outdoor temperature of the city in the next time period obtained from weather forecast information. T ξ is the first temperature correction factor. T ′ is the second temperature correction factor.

[0061] The water flow rate of the target room needs to be determined based on the given range of the opening time of the diversion valve in the target room. A first flow correction factor for each water flow rate value is determined using the same set of fitting coefficients, and a second flow correction factor for each flow rate value is also determined using the same set of fitting coefficients. Both the first and second flow correction factors can be determined, but are not limited to, using another quadratic polynomial sub-model in the energy consumption prediction model. For example, a cubic or higher polynomial can also be used. The expressions for the quadratic polynomial sub-model used to determine the first and second flow correction factors can be found in the following formulas (3) and (4):

[0062] ξ ff =c0+c1ff+c2ff 2 +c3ff 3 (3)

[0063] ξ ff = e0 + e1ff + e2ff 2 +e3ff 3 (4)

[0064] Where c0 to c3 are a set of fitting coefficients used to determine the first flow correction factor in this set of mapping data, and e0 to e3 are a set of fitting coefficients used to determine the second flow correction factor in this set of mapping data, ξ ff ξ is the first flow correction factor. ff ′ represents the second flow correction factor. In one embodiment, the water flow rate value can be either 0 or 1. ff represents the water flow rate value m of the target room. w With nominal water flow rate (m) nominal Ratio:

[0065]

[0066] The first defrosting correction factor and the second defrosting correction factor can be determined, but are not limited to, using a quadratic polynomial sub-model in the energy consumption prediction model, or using a cubic or higher polynomial. The expressions for the quadratic polynomial sub-model used to determine the first flow correction factor and the second flow correction factor can be found in the following formulas (5) and (6):

[0067] ξ frost =b0+b1T db,o +b2T db,o 2 +b3T db,o 3 (5)

[0068] ξ frost ′=g0+g1T db,o +g2T db,o 2 +g3T db,o 3 (6)

[0069] Where b0 to b3 are a set of fitting coefficients used to determine the first defrost correction factor in this set of mapping data, and g0 to g3 are another set of fitting coefficients used to determine the second defrost correction factor in this set of mapping data, T db,o The outdoor temperature can be either the dry-bulb temperature of the outdoor environment or the outdoor temperature of the city in the next time period obtained from weather forecast information.

[0070] It should be noted that in the above formulas (1) to (6), the fitted data of different groups in the same set of mapping data are not the same.

[0071] In some implementations, for each candidate combination of parameter values ​​discretely within a given range of control parameters, a multidimensional energy correction factor is calculated based on each set of mapping data for that candidate combination of parameter values. The energy correction coefficient corresponding to that set of mapping data is then determined based on the product of the multidimensional energy correction factor obtained from the candidate combination of parameter values ​​and the set of mapping data. For example, for each candidate combination of parameter values, the energy correction coefficient for each set of mapping data under that candidate combination of parameter values ​​can be determined by referring to the following sub-model in the energy consumption prediction model, as shown in the following formula (7):

[0072] ξ Q =ξ T *ξ ff *ξ frost (7)

[0073] Where, ξ T ,ξ ff ,ξ frost The following are the energy correction factors for each dimension calculated using a set of mapping data under a candidate combination of parameter values: correction factors related to outdoor temperature and the outlet water temperature of the hydraulic module in the combined heat and power system, correction factors related to the water flow rate of the hydraulic module, and correction factors related to the defrosting process of the outdoor heat exchanger in the combined heat and power system, ξ. Q This is the energy correction factor.

[0074] In some implementations, for each candidate combination of parameter values ​​discrete within a given range of control parameters, a multidimensional energy consumption correction factor is calculated based on each set of mapping data for each candidate combination of parameter values. The energy consumption correction coefficient corresponding to the set of mapping data is determined based on the product of the multidimensional energy consumption correction factor obtained from the candidate combination of parameter values ​​and the set of mapping data. For example, for each candidate combination of parameter values, the energy correction coefficient of each set of mapping data under that candidate combination of parameter values ​​can be determined by referring to the following sub-model in the energy consumption prediction model, as shown in the following formula (8):

[0075] ξ P =ξ T ′*ξ ff ′*ξ frost ′ (8)

[0076] Where, ξ T ′,ξ ff ′,ξ frost ξ represents the energy consumption correction factors for each dimension calculated using a set of mapping data under a candidate combination of parameter values: correction factors related to outdoor temperature and the outlet water temperature of the hydraulic module in the combined heat pump system, correction factors related to the water flow rate of the hydraulic module, and correction factors related to the defrosting function of the outdoor heat exchanger in the combined heat pump system.P This is the energy consumption correction factor.

[0077] In some implementations, the energy calculation value of the set of mapping data is determined based on the nominal energy value of the heat pump dual-supply system and the energy correction coefficient matching the set of mapping data. The calculation formula (9) of the energy consumption prediction model can be referred to below:

[0078] Q = Q nominal *ξ Q (9)

[0079] Among them, Q and ξ Q Q represents the calculated energy value and energy correction coefficient obtained using the mapping data for a candidate combination of parameter values ​​within a given range of control parameters. nominal This is the nominal energy value.

[0080] In some implementations, the energy calculation value of the set of mapping data is determined based on the nominal energy consumption value of the heat pump dual-supply system and the energy consumption correction coefficient that matches the set of mapping data. The calculation formula (10) of the energy consumption prediction model can be referred to below:

[0081] P = P nominal *ξ P (10)

[0082] Among them, P, ξ P For a given range of control parameters, P represents the calculated energy consumption value and energy consumption correction coefficient obtained from the group mapping data for a candidate combination of parameter values. nominal This is the nominal energy consumption value.

[0083] In some implementations, interpolation is performed based on the actual operating frequency and M data pairs to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction results, and the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction results. This includes: performing energy interpolation based on the actual operating frequency and M data pairs to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction results; and performing energy interpolation based on the actual operating frequency and M data pairs to obtain the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction results.

[0084] In some implementations, the interpolation operation can employ any interpolation method, and the number of groups of mapping data in the target mapping data is determined based on the interpolation operation method, which is not limited here.

[0085] In some implementations, the energy prediction result includes the energy prediction value of each candidate combination of parameter values ​​among a variety of discrete candidate combinations of parameter values ​​within a given range of control parameters, and the energy consumption prediction result includes the energy consumption prediction value of each candidate combination of parameter values ​​among a variety of discrete candidate combinations of parameter values ​​within a given range of control parameters, wherein each candidate combination of parameter values ​​is a combination of parameter values ​​of each dimension of control parameters.

[0086] In other embodiments, the energy prediction result is energy change data generated by the continuous variation of the control parameter value within a given range of the control parameter. For example, the energy change data can be a curve showing the amount of energy generated as a function of the control parameter value. The energy consumption prediction result is energy consumption change data generated by the continuous variation of the control parameter value within a given range of the control parameter. For example, the energy change data can be a curve showing the required energy consumption as a function of the control parameter value. This can be achieved by calculating the energy prediction value and energy consumption prediction value for each candidate combination of parameter values ​​from multiple candidate combinations, and then obtaining the energy prediction result and energy consumption prediction result through curve fitting.

[0087] In other implementations, interpolation can be omitted. The compressor's operating frequency range can be pre-divided into multiple frequency bands, and fitting coefficients for each frequency band can be pre-stored to calculate various maintenance positive factors when the compressor's operating frequency is within that band. The pre-stored fitting coefficients for each frequency band include multiple sets, and each dimension's correction factor calculation requires one set of fitting coefficients. For each candidate combination of parameter values, the energy generated and energy consumed by the combined heat pump system based on that parameter value combination in the next time period are predicted. This includes: obtaining the compressor's actual operating frequency in the current time period; determining the target frequency band where the actual operating frequency is located; obtaining the sets of fitting coefficients mapped to the target frequency band from the pre-stored fitting coefficients for each frequency band, as target fitting coefficients; for each candidate combination of parameter values, calculating the energy generated and energy consumed by the combined heat pump system based on that parameter value combination according to the target fitting coefficients to obtain the predicted energy consumption and energy consumption values ​​corresponding to that parameter value combination, without further interpolation. It should be noted that in the implementation method that does not require interpolation, the calculation process for calculating the generated energy and required energy consumption of the heat pump dual-supply system based on the target fitting coefficient and the candidate combination of parameter values ​​is basically the same as the calculation process for calculating each energy value and energy consumption value in the implementation method that requires interpolation. Therefore, the above formulas (1) to (10) can be used as a reference. No further implementation details will be elaborated here. Compared with the implementation method that requires interpolation, the calculation complexity can be reduced.

[0088] S103: Based on the energy prediction results and operational data, predict the room status for the next time period to obtain the room status prediction results.

[0089] In some implementations, room comfort and / or room temperature for the next time period can be predicted based on energy prediction results and operational data to obtain room temperature prediction results and / or room comfort prediction results. The room comfort prediction can be based on a PMV (Predicted Mean Vote) model, such as... Figure 4 As shown, room temperature can be predicted based on a pre-established room temperature prediction model.

[0090] In some implementations, the outdoor temperature for the current time period and the room temperature of the target room are obtained from the operating data. The target room is the room in the heat pump dual-supply system where the water terminal is in use. The energy prediction results, the outdoor temperature for the current time period, and the room temperature of the target room are input into the room temperature prediction model so that the room temperature prediction model can predict the room temperature of the target room in the next time period to obtain the room temperature prediction result. The target room is the room in the heat pump dual-supply system where the water terminal is in the user-activated state. The room temperature prediction result includes the predicted temperature value of each room where the underfloor heating is in the user-activated state.

[0091] In some implementations, the room temperature prediction model is a calculation model based on the principle of energy conservation, which is related to the heat storage characteristics of the target room's envelope, the air energy in the room, and the heat sources in the room.

[0092] In some implementations, the room temperature prediction result includes: for each of a plurality of discrete candidate combinations of parameter values ​​within a given range of control parameters, the predicted room temperature that the target room can achieve. In some implementations, the room temperature prediction model predicts the room temperature of the target room in the next time period, including: for each of a plurality of discrete candidate combinations of parameter values ​​within a given range of control parameters, inputting the predicted energy value obtained from that candidate combination, the outdoor temperature of the current time period, and the room temperature of the target room into the room temperature prediction model, so that the room temperature prediction model predicts the room temperature of the target room in the next time period, to obtain the predicted room temperature that the target room can achieve with that particular candidate combination of parameter values ​​in the room temperature prediction result.

[0093] In some implementations, the expression (11) for the room temperature prediction model established based on the principle of energy conservation is as follows:

[0094]

[0095] Among them, Tk+1 A candidate combination of parameter values ​​is used to predict the room temperature that the target room can achieve; a represents the air energy in the target room, and b represents the energy generated by the heat source in the target room; UA loss k T is a parameter used to characterize the heat storage properties of the building envelope of the target room. o k The outdoor temperature for the current time period is represented by U. More specifically, the U value is the thermal conductivity coefficient determined by the material of the building envelope (including walls, windows, etc.), used to measure the thermal conductivity of the building envelope and represent its ability to allow heat to pass through per unit area. loss This represents the thermal conductivity area of ​​the building envelope.

[0096] In some implementations, the values ​​of a and b are referenced to the following expressions (12) and (13):

[0097]

[0098]

[0099] Where Δt is the time step, M is the air mass, and c p,air The specific heat capacity of air. It is an energy prediction value obtained through a candidate combination of parameter values ​​predicted by an energy consumption model. The heat generated by other heat sources in the room is constant, therefore... It can be a constant.

[0100] The room temperature prediction model based on the above expressions (11) to (13) can take into account the heat storage characteristics of the building envelope and the operating status of the combined heat pump system, further improving the optimization effect of control parameters, further enabling the unit to operate in a higher energy efficiency zone, and further reducing power consumption.

[0101] It should be noted that, according to the principle of energy conservation, the temperature control equation for each room is as follows:

[0102]

[0103] Where M, c p,air Q represents the mass and specific heat capacity of the air in the room, T represents time, and Q represents the mass and specific heat capacity of the air in the room. w Q loss Q heat The components are, in order, the heating energy supplied by the heat pump dual-supply system, the energy lost, and the heat generated by the heat source in the room.

[0104] Q w Q loss Q heatThe calculation methods are as follows:

[0105] Q w =θ·mc p,w (T SW -T RW (15)

[0106]

[0107] Where θ represents the influence of the floor's heat storage characteristics, estimated by the time constant tau. p,w T represents the mass and specific heat capacity of the water in the hydraulic module. SW and T RW These are the outlet and return water temperatures in the hydraulic module.

[0108] Q loss =UA loss (T o -T t (17)

[0109] Among them, U, A loss T represents the thermal conductivity and thermal conductivity area of ​​the building envelope. o T t The outdoor temperature and the room temperature are listed in order.

[0110] Since multiple rooms n correspond to one hydraulic module, the temperature control equation becomes:

[0111]

[0112] Discretizing the temperature control equations allows us to estimate the heat loss for the current time period:

[0113]

[0114] Then we have:

[0115]

[0116] Then the room temperature T in the next time period, i.e., time period k+1. k+1 The prediction is:

[0117]

[0118] The expression (11) of the room temperature prediction model can be obtained by deriving the above formulas (14) to (21). The room temperature prediction model can quickly predict the room temperature.

[0119] S104: Optimize parameters based on energy consumption prediction results and room status prediction results to determine target control parameters, and control the operation of the heat pump dual-supply system according to the target control parameters in the next time period.

[0120] In some implementations, the energy consumption prediction results and room condition prediction results are input into a pre-established optimization model, so that the optimization model takes the room temperature within a given temperature range as a constraint and the minimum energy consumption of the heat pump dual-supply system as the optimization objective. Based on the energy consumption prediction results and room condition prediction results, parameter optimization calculations are performed on the given range of control parameters to obtain the target control parameters within the given range of control parameters.

[0121] The optimization model can employ any relevant technique. In some implementations, the optimization model can eliminate candidate combinations of parameter values ​​whose predicted room temperature is outside the given temperature range and / or whose predicted room comfort is outside the given comfort range, and sort the remaining candidate combinations of parameter values ​​based on their predicted energy consumption values. The candidate combination of parameter values ​​with the lowest predicted energy consumption value is then selected as the target control parameter from the sorted results.

[0122] In some implementations, during the initial period after the combined heat pump and cooling system is started, the system is controlled to operate with pre-defined initial control parameters to facilitate the acquisition of operating data during this initial period. After the initial period ends, steps S101 to S104 are entered to put the system parameters into an optimized control mode. By setting reasonable time steps (e.g., 10 minutes, 20 minutes) in the optimized control mode, the start and stop of the water terminals in the combined heat pump and cooling system are precisely adjusted, and the outlet water temperature setpoint of the hydraulic module is continuously optimized. This makes the unit operation more consistent with actual operating conditions, resulting in higher energy efficiency and ensuring room comfort, thus achieving an overall comfortable and energy-saving effect for the system.

[0123] In some implementations, the initial control parameters can be user-input control parameters or default control parameters.

[0124] In scenarios executed by cloud or edge devices, the cloud or edge devices send target control parameters to the controller of the combined heat pump and cooling system. The controller then executes the received target control parameters to control the operation of the combined heat pump and cooling system in the next time period. The operation control method for combined heat pump and cooling provided in this invention, being an active predictive control rather than a passive feedback response, can effectively meet user needs and has good flexibility. It also improves the intelligence level of air-fluidized water heating.

[0125] To facilitate the explanation of the effectiveness of the operation control method for the combined heat pump and cooling system provided in the embodiments of the present invention, a comparative example between related technologies and the present invention is given below (operation control based on a 7A modified environmental laboratory model):

[0126] Known system configuration: "Leaving Home / Returning Home" mode (leaving home between 8:00 AM and 8:00 PM); the allowable room temperature to drop to 18°C ​​during the "leaving home" period; outdoor temperature based on typical meteorological parameters for a specific month in a specific city; relevant technologies: a hydraulic module provides water at a fixed 45°C; and the room temperature is maintained at 22°C ± 1°C. A comparison of room temperature, room PMV, and energy consumption between relevant technologies and this invention is shown in Table 1 below.

[0127] Table 1. Comparison of energy consumption and room conditions between related technologies and the technical solution of this invention.

[0128] Related Art The Invention Comparison Results First Day Energy Consumption (Kwh) 31.44 24.83 -21％ Total Three Day Energy Consumption (Kwh) 94.17 67.58 -28％ Average Temperature Upon Return Home for Four Hours (°C) 22.36 23.40 +1.04 Average PMV Upon Return Home for Four Hours 0.061 0.064 +0.003

[0129] Figure 5A , Figure 5B and Figure 5C The table above compares the room temperature, room PMV, and outlet water temperature of the hydraulic module with those of the present invention and related technologies. Figures 5A-5C As shown, compared with the related technology of water supply at a fixed 45°C by hydraulic modules, the operation control method of the heat pump dual-supply system provided in this embodiment of the invention has lower energy consumption, higher room temperature, and higher comfort.

[0130] In some implementations, in scenarios where user terminals communicate with cloud devices (or edge devices) via gateways, the operating data, target control parameters, and various prediction results of the combined heat pump and power system can be transmitted to the user terminals via the network, enhancing the user's understanding and interaction with the system. In some implementations, the cloud devices (or edge devices) also predict system performance and feed it back to the user terminals and / or the combined heat pump and power system.

[0131] Based on the same inventive concept, embodiments of the present invention also provide an operation control device for a combined heat pump and combined cooling, heating, and power system, such as... Figure 6 As shown, Figure 6The structure of an operation control device for a heat pump combined heat and power system in some embodiments of the present invention is shown. The operation control device includes: a data acquisition unit 601, used to acquire the operation data of the heat pump combined heat and power system in the current time period; a first prediction unit 602, used to predict the energy generated and energy consumption required by the heat pump combined heat and power system in the next time period based on the operation data, to obtain energy prediction results and energy consumption prediction results; a second prediction unit 603, used to predict the room status in the next time period based on the energy prediction results and the operation data, to obtain room status prediction results; an optimization unit 604, used to optimize parameters based on the energy consumption prediction results and the room status prediction results to determine target control parameters; and a control unit 605, used to control the operation of the heat pump combined heat and power system in the next time period according to the target control parameters.

[0132] In some embodiments, the first prediction unit 602 includes: a range-given subunit, used to determine the range of control parameters for the next time period based on the operating data; and a prediction execution subunit, used to predict the energy generated and energy consumption required by the combined heat pump and power system in the next time period based on the range of control parameters, so as to obtain the energy prediction result and the energy consumption prediction result.

[0133] In some implementations, the prediction execution subunit includes: a combination generation module, configured to generate various candidate combinations of parameter values ​​for the control parameters within a given range of the control parameters; and a single-value prediction module, configured to predict the energy generated and energy consumption required by the heat pump combined heat and power system in the next time period based on each candidate combination of parameter values, so as to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction results, and the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction results.

[0134] In some implementations, the single-value prediction module includes: a data acquisition submodule, used to acquire target mapping data from pre-stored mapping data, wherein the target mapping data is a mapping between multiple target frequency points and fitting coefficients, the multiple target frequency points being related to the actual operating frequency of the compressor of the combined heat pump and power system in the current time period, and the operating data including the actual operating frequency; a data pair obtaining submodule, used to predict the energy generated and required energy consumption of the combined heat pump and power system based on the target mapping data for each candidate combination of parameter values, to obtain M data pairs, wherein each data pair corresponds to a frequency point and a calculated energy value and a calculated energy consumption value, and M is an integer greater than 1; and an interpolation operation submodule, used to perform interpolation operations based on the actual operating frequency and the M data pairs to obtain the predicted energy value corresponding to the candidate combination of parameter values ​​in the energy prediction result, and the predicted energy consumption value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction result.

[0135] In some implementations, a data pairing submodule is used to determine, for each set of mapping data in the target mapping data, a correction coefficient matching the set of mapping data based on the candidate combination of such parameter values, and to determine a data pair matching the set of mapping data based on the nominal value of the heat pump dual-supply system and the correction coefficient matching the set of mapping data.

[0136] In some implementations, the correction coefficients matching each set of mapping data in the target mapping data include an energy correction coefficient and an energy consumption correction coefficient. The data pair obtaining submodule is used to: for each set of mapping data in the target mapping data, determine the calculated energy value of the set of mapping data based on the nominal energy value of the heat pump combined heat and power system and the energy correction coefficient matching that set of mapping data; determine the calculated energy consumption value of the set of mapping data based on the nominal energy consumption value of the heat pump combined heat and power system and the energy consumption correction coefficient matching that set of mapping data; and for each set of mapping data in the target mapping data, obtain a data pair matching that set of mapping data based on the target frequency point in that set of mapping data and the calculated energy value and calculated energy consumption value determined based on that set of mapping data.

[0137] In some implementations, the interpolation operation submodule is used to: perform energy interpolation calculations based on the actual operating frequency and the M data pairs to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction result; and perform energy consumption interpolation calculations based on the actual operating frequency and the M data pairs to obtain the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction result.

[0138] In some implementations, the data pairing submodule is used to: obtain the outdoor temperature for the current time period from the operating data; for each set of mapping data in the target mapping data, determine a multidimensional energy correction factor and a multidimensional energy consumption correction factor that affect the energy generated by the heat pump combined heat and power system based on the outdoor temperature for the current time period, the fitting coefficient in the set of mapping data, and the candidate combination of parameter values ​​within the given range of the control parameters; for each set of mapping data in the target mapping data, determine the energy correction coefficient that matches the set of mapping data based on the multidimensional energy correction factor obtained from the set of mapping data, and determine the energy consumption correction coefficient that matches the set of mapping data based on the multidimensional energy consumption correction factor obtained from the set of mapping data.

[0139] In some embodiments, both the multidimensional energy correction factor and the multidimensional energy consumption correction factor include:

[0140] Correction factors related to outdoor temperature and outlet water temperature of the hydraulic module in the combined heat pump system, correction factors related to water flow rate of the hydraulic module, and correction factors related to defrosting process of outdoor heat exchanger in the combined heat pump system.

[0141] In some embodiments, the room status prediction result includes a room temperature prediction result; the first prediction unit includes: an acquisition subunit, used to acquire the outdoor temperature of the current time period and the room temperature of the target room in the current time period from the operating data, wherein the target room is the room in the heat pump dual-supply system where the water terminal is in the user-activated state; and a temperature prediction subunit, used to input the energy prediction result, the outdoor temperature of the current time period, and the room temperature of the target room into the room temperature prediction model, so that the room temperature prediction model predicts the room temperature of the target room in the next time period to obtain the room temperature prediction result.

[0142] In some implementations, the room temperature prediction model is a calculation model based on the principle of energy conservation, which is related to the heat storage characteristics of the building envelope of the target room, the air energy in the room, and the heat sources in the room.

[0143] In some implementations, the room temperature prediction model is expressed as follows:

[0144]

[0145] Among them, T k+1A candidate combination of parameter values ​​is used to predict the room temperature that the target room can achieve; a is the air energy in the target room, and b is a parameter related to the energy prediction result and the energy generated by the heat source in the target room; UA loss k T is a parameter characterizing the heat storage properties of the building envelope of the target room. o k This represents the outdoor temperature for the current time period.

[0146] In some implementations, the optimization unit 604 is used to: input the energy consumption prediction result and the room state prediction result into a pre-established optimization model, so that the optimization model takes the room temperature within a given temperature range as a constraint and the minimum energy consumption of the heat pump dual-supply system as the optimization objective, and performs parameter optimization calculation on the given range of the control parameters based on the energy consumption prediction result and the room state prediction result, so as to obtain the target control parameters within the given range of the control parameters.

[0147] The specific functions of each functional unit in the above-mentioned device have been described in detail in the operation control method of the heat pump dual-supply system provided in some embodiments of the present invention, and will not be elaborated here.

[0148] Based on the same inventive concept, this invention also provides a combined heat pump and cooling system, such as... Figure 1 As shown, the heat pump dual-supply system includes: an outdoor unit; a hydraulic module and multiple air conditioning terminals, each connected to the outdoor unit; and multiple water terminals, connected to the outdoor unit via the hydraulic module; as shown... Figure 7 As shown, Figure 7 The control structure of a heat pump dual-supply system in some embodiments of the present invention is shown. The heat pump dual-supply system further includes: a processor 702; and a memory 704 for storing executable instructions of the processor 702, wherein the processor 702 is configured to execute the instructions to implement the operation control method of the heat pump dual-supply system described in any of the above embodiments.

[0149] Among them, Figure 7In this document, a bus architecture (represented by bus 700) is used. Bus 700 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 702 and memory represented by memory 704. Bus 700 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 705 provides an interface between bus 700 and receiver 701 and transmitter 703. Receiver 701 and transmitter 703 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 702 is responsible for managing bus 700 and general processing, while memory 704 can be used to store data used by processor 702 during operation.

[0150] Based on the same inventive concept, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the operation control method of the heat pump dual-supply system described in any of the above embodiments.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure One One or more processes and / or boxes Figure One Devices that specify the functions in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction device, which is implemented in a process Figure One One or more processes and / or boxes Figure One The function specified in one or more boxes.

[0153] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure OneOne or more processes and / or boxes Figure One The steps of the function specified in one or more boxes.

[0154] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0155] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0156] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for operating and controlling a heat pump dual-supply system, characterized in that, include: Obtain the operating data of the heat pump dual-supply system in the current time period; Based on the operating data, the energy generated and energy consumption required by the heat pump dual-supply system in the next time period are predicted to obtain energy prediction results and energy consumption prediction results. Based on the energy prediction results and the operational data, the room status for the next time period is predicted to obtain the room status prediction results. Based on the energy consumption prediction results and the room status prediction results, the parameters are optimized to determine the target control parameters, and the heat pump dual-supply system is controlled to operate according to the target control parameters in the next time period.

2. The method as described in claim 1, characterized in that, The step of predicting the energy generated and energy consumption required by the heat pump combined heat and power system in the next time period based on the operating data, to obtain energy prediction results and energy consumption prediction results, includes: The given range of control parameters for the next time period is determined based on the operational data. The energy generated and energy consumption required by the heat pump dual-supply system during the next time period are predicted based on the given range of the control parameters, so as to obtain the energy prediction result and the energy consumption prediction result.

3. The method as described in claim 2, characterized in that, The prediction of the energy generated and energy consumption required by the combined heat pump and power system in the next time period based on the given range of control parameters, to obtain the energy prediction result and the energy consumption prediction result, includes: Within the given range of the control parameters, various candidate combinations of control parameter values ​​are generated; For each candidate combination of the parameter values, the energy generated and energy consumption required by the heat pump dual-supply system in the next time period based on the candidate combination of the parameter values ​​are predicted, so as to obtain the energy prediction value corresponding to the candidate combination of the parameter values ​​in the energy prediction result, and the energy consumption prediction value corresponding to the candidate combination of the parameter values ​​in the energy consumption prediction result.

4. The method as described in claim 3, characterized in that, For each candidate combination of the aforementioned parameter values, the energy generated and energy consumed by the combined heat pump and power system in the next time period based on that candidate combination of parameter values ​​are predicted, including: Obtain target mapping data from pre-stored mapping data, wherein the target mapping data is a mapping between multiple target frequency points and fitting coefficients, the multiple target frequency points are related to the actual operating frequency of the compressor of the heat pump dual-supply system in the current time period, and the operating data includes the actual operating frequency; For each candidate combination of parameter values, the energy generated and energy consumption required by the heat pump dual-supply system based on the target mapping data are predicted to obtain M data pairs, where each data pair corresponds to a frequency point and an energy calculation value and an energy consumption calculation value, and M is an integer greater than 1. Interpolation is performed based on the actual operating frequency and the M data pairs to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction result, and the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction result.

5. The method as described in claim 4, characterized in that, The target mapping data includes M sets of mapping data. The prediction of the generated energy and required energy consumption of the combined heat pump and power system based on this candidate combination of parameter values ​​yields M data pairs, including: For each set of mapping data in the target mapping data, a correction coefficient matching the set of mapping data is determined based on the candidate combination of such parameter values, and a data pair matching the set of mapping data is determined based on the nominal value of the heat pump dual-supply system and the correction coefficient matching the set of mapping data.

6. The method as described in claim 5, characterized in that, The correction coefficients matched for each set of mapping data in the target mapping data include an energy correction coefficient and an energy consumption correction coefficient. The step of determining a data pair that matches the set of mapping data based on the nominal value of the combined heat pump and cooling system and the correction coefficient that matches the set of mapping data includes: For each set of mapping data in the target mapping data, the energy calculation value of the set of mapping data is determined according to the nominal energy value of the heat pump dual-supply system and the energy correction coefficient matching the set of mapping data. The energy consumption calculation value of the set of mapping data is determined according to the nominal energy consumption value of the heat pump dual-supply system and the energy consumption correction coefficient matching the set of mapping data. For each set of mapping data in the target mapping data, a data pair matching the set of mapping data is obtained based on the target frequency point in the set of mapping data and the energy calculation value and energy consumption calculation value determined based on the set of mapping data.

7. The method as described in claim 6, characterized in that, The step of performing interpolation based on the actual operating frequency and the M data pairs to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction result, and the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction result, includes: Based on the actual operating frequency and the M data pairs, an energy interpolation operation is performed to obtain the energy prediction value corresponding to the candidate combination of parameter values ​​in the energy prediction result; Based on the actual operating frequency and the M data pairs, an interpolation operation is performed on the energy consumption to obtain the energy consumption prediction value corresponding to the candidate combination of parameter values ​​in the energy consumption prediction result.

8. The method as described in claim 6, characterized in that, The step of determining the correction coefficient matching the target mapping data for each group of mapping data based on the candidate combination of parameter values ​​includes: Obtain the outdoor temperature for the current time period from the operational data; For each set of mapping data in the target mapping data, based on the outdoor temperature of the current time period, the fitting coefficient in the set of mapping data, and the candidate combination of parameter values ​​within the given range of the control parameters, a multidimensional energy correction factor and a multidimensional energy consumption correction factor are determined to affect the energy generated by the heat pump dual-supply system based on the candidate combination of parameter values. For each set of mapping data in the target mapping data, the energy correction coefficient matching the set of mapping data is determined based on the multidimensional energy correction factor obtained from the set of mapping data, and the energy consumption correction coefficient matching the set of mapping data is determined based on the multidimensional energy consumption correction factor obtained from the set of mapping data.

9. The method as described in claim 8, characterized in that, Both the multidimensional energy correction factor and the multidimensional energy consumption correction factor include: Correction factors related to outdoor temperature and outlet water temperature of the hydraulic module in the combined heat pump system, correction factors related to water flow rate of the hydraulic module, and correction factors related to defrosting process of outdoor heat exchanger in the combined heat pump system.

10. The method as described in claim 1, characterized in that, The room condition prediction results include the room temperature prediction results; The step of predicting the room status for the next time period based on the energy prediction result and the operational data to obtain the room status prediction result includes: The outdoor temperature and the room temperature of the target room during the current time period are obtained from the operation data. The target room is the room in the heat pump dual supply system where the water terminal is activated by the user. The energy prediction result, the outdoor temperature of the current time period, and the room temperature of the target room are input into the room temperature prediction model so that the room temperature prediction model can predict the room temperature of the target room in the next time period to obtain the room temperature prediction result.

11. The method as described in claim 10, characterized in that, The room temperature prediction model is established based on the principle of energy conservation and is a calculation model that is related to the heat storage characteristics of the building envelope of the target room, the air energy in the room, and the heat sources in the room.

12. The method as described in claim 11, characterized in that, The expression for the room temperature prediction model is as follows: Among them, T k+1 A candidate combination of parameter values ​​is used to predict the room temperature that the target room can achieve; a is the air energy in the target room, and b is a parameter related to the energy prediction result and the energy generated by the heat source in the target room; UA loss k T is a parameter characterizing the heat storage properties of the building envelope of the target room. o k This represents the outdoor temperature for the current time period.

13. The method as described in claim 2, characterized in that, The step of optimizing parameters based on the energy consumption prediction results and the room status prediction results to determine the target control parameters includes: The energy consumption prediction results and the room status prediction results are input into a pre-established optimization model, so that the optimization model takes the room temperature within a given temperature range as a constraint and the minimum energy consumption of the heat pump dual-supply system as the optimization objective. Based on the energy consumption prediction results and the room status prediction results, parameter optimization calculations are performed on the given range of control parameters to obtain the target control parameters within the given range of control parameters.

14. An operation control device for a heat pump dual-supply system, characterized in that, include: The data acquisition unit is used to acquire the operating data of the heat pump dual-supply system in the current time period; The first prediction unit is used to predict the energy generated and energy consumption required by the heat pump dual-supply system in the next time period based on the operating data, so as to obtain the energy prediction result and the energy consumption prediction result. The second prediction unit is used to predict the room status for the next time period based on the energy prediction result and the operating data, so as to obtain the room status prediction result. The optimization unit is used to optimize parameters based on the energy consumption prediction results and the room status prediction results to determine the target control parameters. The control unit is used to control the operation of the heat pump dual-supply system according to the target control parameters in the next time period.

15. A heat pump dual-supply system, characterized in that, include: Outdoor unit; The hydraulic module and multiple air conditioning terminals are respectively connected to the outdoor unit; Multiple water terminals are connected to the outdoor unit via the hydraulic module; processor; A memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the operation control method for a heat pump dual-supply system as described in any one of claims 1 to 13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the operation control method of the heat pump dual-supply system as described in any one of claims 1 to 13.

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